System and Method to Predict Service Level Failure in Supply Chains

ABSTRACT

A system and method are disclosed for receiving only historical supply chain data from an archiving system for one or more supply chain entities storing items at stocking locations, predicting one or more supply chain events during a prediction period by applying a prediction model to a sample of historical supply chain data, calculating an occurrence risk score for at least one of the one or more supply chain events and indicating a possibility that the at least one of the one or more supply chain events will occur, generating one or more alerts identifying at least one item and at least one alert stocking location, rendering an alert heatmap visualization comprising one or more selectable user interface elements, and provide one or more tools for initiating corrective actions to be undertaken in order to resolve one or more underlying causes of the at least one alert supply chain event.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/167,224, filed on Oct. 22, 2018, entitled “System and Method toPredict Service Level Failure in Supply Chains.” U.S. patent applicationSer. No. 16/167,224 is assigned to the assignee of the presentapplication.

TECHNICAL FIELD

The present disclosure relates generally to supply chain management andspecifically to systems and methods for predicting and resolving servicelevel failures in a supply chain.

BACKGROUND

Although a goal of supply chain planning is to generateglobally-optimized supply chain plans for an entire business, thecalculation and execution of supply chain plans is typically controlledby several distinct and dissimilar processes, including, for example,demand planning, production planning, supply planning, distributionplanning, execution, and the like. Each of these processes may havediffering data requirements and planning periods which makessynchronizing the processes difficult. Often a demand or productionvariation is detected after the planning period of one or more processesand prevents generating a globally-optimized plan that accounts for thevariation. The failure to account for variation in the globallyoptimized plan may cause service failures and prevents achievingcustomer service level targets. This result is undesirable.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present invention may be derived byreferring to the detailed description when considered in connection withthe following illustrative figures. In the figures, like referencenumbers refer to like elements or acts throughout the figures.

FIG. 1 illustrates an exemplary supply chain network, according to anembodiment;

FIG. 2 illustrates the visualization system and the supply chain eventpredictor of FIG. 1 in more detail, in accordance with an embodiment;

FIGS. 3A-F illustrate a master visualization dashboard, according to anembodiment;

FIGS. 4A-B illustrate the alert prediction overview of FIGS. 3A-F inmore detail, in accordance with an embodiment;

FIGS. 5A-B illustrate the interactivity one or more alert elements ofFIGS. 4A-B in more detail, in accordance with an embodiment;

FIG. 6 illustrates the alert priority visualizer of FIGS. 3A-F in moredetail, in accordance with an embodiment;

FIG. 7 illustrates the selected alert overview of FIGS. 3A-F in moredetail, in accordance with an embodiment;

FIG. 8 illustrates the alert features contributions visualization ofFIG. 7 , in more detail according to embodiment;

FIG. 9 illustrates a temporal alert features contributionsvisualization, according to an embodiment;

FIG. 10 illustrates the location overview visualization of FIGS. 3A-F inmore detail, in accordance with an embodiment;

FIG. 11 illustrates the production overview visualization of FIGS. 3A-Fin more detail, in accordance with an embodiment; and

FIGS. 12A-B illustrate the logistics overview visualization of FIGS.3A-F in more detail, in accordance with an embodiment.

DETAILED DESCRIPTION

Aspects and applications of the invention presented herein are describedbelow in the drawings and detailed description of the invention. Unlessspecifically noted, it is intended that the words and phrases in thespecification and the claims be given their plain, ordinary, andaccustomed meaning to those of ordinary skill in the applicable arts.

In the following description, and for the purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of the various aspects of the invention. It will beunderstood, however, by those skilled in the relevant arts, that thepresent invention may be practiced without these specific details. Inother instances, known structures and devices are shown or discussedmore generally in order to avoid obscuring the invention. In many cases,a description of the operation is sufficient to enable one to implementthe various forms of the invention, particularly when the operation isto be implemented in software. It should be noted that there are manydifferent and alternative configurations, devices and technologies towhich the disclosed inventions may be applied. The full scope of theinventions is not limited to the examples that are described below.

Supply chains often fail to fully meet the intended targets set by asupply chain plan or may need to adjust to elements that had not beenforecasted in the initial plan. Each of the supply chain planning andexecution processes may have differing data requirements and planningperiods which prevents fully synchronizing the various planning andexecution processes of the supply chain. When particular events occur,such as a sudden change in demand or production, a transportation delay,and/or one or more shipping problems, the events may be detected toolate to generate a new plan that accounts for the event. However one ormore supply chain planners may still be able to take actions that wouldprevent a failure that would otherwise be caused by the event. As such,with a system that identifies events within the planning horizon abusiness may take a prescriptive approach (preventing problems beforethey occur) as opposed to what is ordinarily done, where a businessreacts to failures only once they have already occurred.

Embodiments disclosed below apply machine learning techniques toarchived supply chain data to predict supply chain failures before theyoccur, generate alerts and contextual visualizations that identify theunderlying causes of the predicted failures, and provide situationalawareness of the past and predicted state of the supply chain affectedby the predicted failure. Some embodiments of the disclosed systemprovide for a single interface that generates alerts for predictedsupply chain failures without using real-time data and without access toproduction system or transportation management system rules. Asdescribed in more detail below, by using only historical supply chaindata, embodiments predict supply chain failures without aligning data orevent models and without integration with planning and executionprocesses, which reduces computational run time of the computerprocessing and implementation time and costs.

FIG. 1 illustrates exemplary supply chain network 100, according to anembodiment. Supply chain network 100 comprises visualization system 110,supply chain event predictor 120, archiving system 130, one or moreplanning and execution systems 140, one or more supply chain entities150, inventory system 160, transportation network 170, computer 180,network 190, and communication links 192 a-192 c and 194 a-194 fAlthough a single visualization system 110, a single supply chain eventpredictor 120, a single archiving system 130, one or more planning andexecution systems 140, one or more supply chain entities 150, a singleinventory system 160, a single transportation network 170, a singlecomputer 180, and a single network 190, are shown and described,embodiments contemplate any number of visualization systems, supplychain event predictors, archiving systems, one or more planning andexecution systems, supply chain entities, inventory system,transportation network, computers, or networks, according to particularneeds.

In one embodiment, visualization system 110 comprises server 112 anddatabase 114. As explained in more detail below, server 112 comprisesone or more modules that receive predictions for service level failuresand generate alerts that identify the item, location, and time when aservice level failure is predicted to occur. According to embodiments,visualization system 110 provides a visualization user interface (UI)that filters, hides, and sorts alerts to prioritize alerts related to,for example, important customers, high-value or large-volume products,and de-prioritize alerts that are redundant or unneeded. In addition,visualization system 110 displays contextual supply chain data thatprovides situational awareness of the past and predicted state of one ormore supply chain entities 150 associated with each alert. For example,when an alert is selected using the visualization UI, visualizationsystem 110 displays charts, graphs, data, and the like which summarizeand elaborate on one or more aspects of the variables used to predictthe service level failure identified by the selected alert, which mayinclude, for example, order timing and quantity, inventory levels, thequantity or percentage of on-time shipments, and other supply chainmeasurements, as described in more detail below. According toembodiments, visualization system 110 receives service level failurepredictions from supply chain event predictor 120, as described in moredetail below.

In one embodiment, supply chain event predictor 120 comprises server 122and database 124. Server 122 of supply chain event predictor 120comprises one or more modules that model predictions of supply chainevents, such a service level failure, as a classification problem usinga machine learning model (such as, for example, a gradient boosted treesmachine learning model, neural networks, and the like), trains the modelwith snapshots of historical supply chain data retrieved from archivingsystem 130, and predicts supply chain events for future time periods byapplying snapshots of recent supply chain data to the trained model.Supply chain event predictor 120 transmits the supply chain eventpredictions to visualization system 110 which generates and displaysalerts based on the supply chain events. As described in more detailbelow, supply chain event predictor 120 predicts supply chain events,such as service level failures or order promising failures, that occurtoo late to be included on a current plan but before the time periodcovered by the next plan.

As discussed above, supply chain event predictor 120 predicts the supplychain events using only historical supply chain data. According toembodiments, supply chain event predictor 120 retrieves the historicalsupply chain data from database 134 of archiving system 130 and storesthe historical supply chain data at database 124. Supply chain eventpredictor 120 may process, transform, and normalize the historicalsupply chain data to prepare the data for use with one or more machinelearning models. After the historical supply chain data is processed,supply chain event predictor 120 applies machine learning techniques tothe processed historical supply chain data to predict future supplychain events, including, for example, service level failures, futurestock outs, returns, and the like. As described in more detail below,supply chain event predictor 120 generates the prediction for the supplychain event much earlier than the event is predicted to occur, whichprovides an opportunity for one or more supply chain entities 150 toresolve the supply chain event by initiating one more levers, such as,for example, changing safety stock, adjusting a forecast, and the like.

Archiving system 130 of supply chain network 100 comprises server 132and database 134. Although archiving system 130 is shown as comprising asingle server 132 and a single database 134, embodiments contemplate anysuitable number of servers or databases internal to or externallycoupled with archiving system 130. Server 132 of archiving system 130may support one or more processes for receiving historical supply chaindata from one or more supply chain planning and execution processes 140.Historical supply chain data may include, for example, sales data,forecast data, stock levels, safety stock levels, forecast production,logistics operations, such as, for example, the predicted time to ship aproduct from a first location to a second location, actual shippingtimes for past shipments, production plans, actual production, storedata (including sales numbers (e.g. number of sales over the past week),the start level at the store, the quality of the forecasts for a store),data from one or more planning and execution systems 140, including, forexample, a transportation management system (TMS), a warehousemanagement system (WMS), fulfillment system, procurement system,production systems, and the like.

Server 132 may store the received historical supply chain data indatabase 134. Database 134 of archiving system 130 may comprise one ormore databases or other data storage arrangement at one or morelocations, local to, or remote from, server 132 storing supply chaindata of the supply chain network 100.

According to an embodiment, one or more planning and execution systems140 comprise server 142 and database 144. As described above, supplychain planning and execution is typically performed by several distinctand dissimilar processes, including, for example, demand planning,production planning, supply planning, distribution planning, execution,transportation management, warehouse management, fulfilment, and thelike. Server 142 of one or more planning and execution systems 140comprises one or more modules, such as, for example, a planning module,a solver, a modeler, and/or an engine, for performing actions of one ormore planning and execution processes. Server 142 stores and retrievessupply chain data from database 144 or from one or more locations insupply chain network 100. In addition, one or more planning andexecution systems 140 operate on one or more computers 180 that areintegral to or separate from the hardware and/or software that supportarchiving system 130, one or more supply chain entities 150, inventorysystem 160, and transportation network 170.

Inventory system 160 comprises server 162 and database 164. Server 162of inventory system 160 is configured to receive and transmit item data,including item identifiers, pricing data, attribute data, inventorylevels, and other like data about one or more items at one or morelocations in the supply chain network 100. Server 162 stores andretrieves item data from database 164 or from one or more locations insupply chain network 100. Each item may be represented in supply chainnetwork 100 by an identifier, including, for example, Stock-Keeping Unit(SKU), Universal Product Code (UPC), serial number, barcode, tag, aradio-frequency identification (RFID) tag, or like objects that encodeidentifying information and which may be scanned to read the encodedinformation and at least partially identified based on the scan. Thismay include, for example, a stationary scanner located at one or moresupply chain entities 150 that scans items as the items pass near thescanner such as, for example, a point of sale system at one or moreretailers that records transactions and associates the transactions withproduct data, including, for example, associating customer identityinformation, store identity and location, market information, timeinformation, price information, discount information, and the like, asdescribed in more detail herein. Embodiments also include, for example,a scanner located at one or more stocking locations of one or moresupply chain entities 150 that automatically identifies when an item isreceived into or removed from the one or more stocking locations.

Transportation network 170 comprises server 172 and database 174.According to embodiments, transportation network 170 directs one or moretransportation vehicles to ship one or more items between one or moresupply chain entities 150, based, at least in part, on a predicatedsupply chain event, predicted order promise failure, predicted servicelevel failure, an inventory policy, target service levels, the number ofitems currently in stock at one or more supply chain entities 150, thenumber of items currently in transit in the transportation network 170,forecasted demand, a supply chain disruption, and/or one or more otherfactors described herein. Transportation vehicles comprise, for example,any number of trucks, cars, vans, boats, airplanes, unmanned aerialvehicles (UAVs), cranes, robotic machinery, or the like. Transportationvehicles may comprise radio, satellite, or other communication thatcommunicates location information (such as, for example, geographiccoordinates, distance from a location, global positioning satellite(GPS) information, or the like) with visualization system 110, supplychain event predictor 120, archiving system 130, one or more planningand execution systems 140, one or more supply chain entities 150, and/orinventory system 160, to identify the location of the transportationvehicle and the location of any inventory or shipment located on thetransportation vehicle.

As shown in FIG. 1 , supply chain network 100 operates on one or morecomputers 180 that are integral to or separate from the hardware and/orsoftware that support visualization system 110, supply chain eventpredictor 120, archiving system 130, one or more planning and executionsystems 140, one or more supply chain entities 150, inventory system160, and transportation network 170. Supply chain network 100 comprisingvisualization system 110, supply chain event predictor 120, archivingsystem 130, one or more planning and execution systems 140, one or moresupply chain entities 150, inventory system 160, and transportationnetwork 170 may operate on one or more computers 160 that are integralto or separate from the hardware and/or software that supportvisualization system 110, supply chain event predictor 120, archivingsystem 130, one or more planning and execution systems 140, one or moresupply chain entities 150, inventory system 160, and transportationnetwork 170. Computers 180 may include any suitable input device 182,such as a keypad, mouse, touch screen, microphone, or other device toinput information. Output device 184 may convey information associatedwith the operation of supply chain network 100, including digital oranalog data, visual information, or audio information. Computer 180 mayinclude fixed or removable computer-readable storage media, including anon-transitory computer readable medium, magnetic computer disks, flashdrives, CD-ROM, in-memory device or other suitable media to receiveoutput from and provide input to supply chain network 100.

Computer 180 may include one or more processors 186 and associatedmemory to execute instructions and manipulate information according tothe operation of supply chain network 100 and any of the methodsdescribed herein. In addition, or as an alternative, embodimentscontemplate executing the instructions on computer 180 that causecomputer 180 to perform functions of the method. Further examples mayalso include articles of manufacture including tangible non-transitorycomputer-readable media that have computer-readable instructions encodedthereon, and the instructions may comprise instructions to performfunctions of the methods described herein.

In addition, and as discussed herein, supply chain network 100 maycomprise a cloud-based computing system having processing and storagedevices at one or more locations, local to, or remote from visualizationsystem 110, supply chain event predictor 120, archiving system 130, oneor more planning and execution systems 140, one or more supply chainentities 150, inventory system 160, and transportation network 170. Inaddition, each of the one or more computers 180 may be a work station,personal computer (PC), network computer, notebook computer, tablet,personal digital assistant (PDA), cell phone, telephone, smartphone,wireless data port, augmented or virtual reality headset, or any othersuitable computing device. In an embodiment, one or more users may beassociated with the visualization system 110, supply chain eventpredictor 120, archiving system 130, one or more planning and executionsystems 140, one or more supply chain entities 150, inventory system160, and transportation network 170. These one or more users mayinclude, for example, a “manager” or a “planner” handling supply chainevent prediction, service level failure prediction, supply chainplanning, actions following supply chain events, actions in response toalerts for predicted supply chain events, and/or one or more relatedtasks within supply chain network 100. In addition, or as analternative, these one or more users within supply chain network 100 mayinclude, for example, one or more computers programmed to autonomouslyhandle, among other things, determining an assortment plan, demandforecasting demand, supply and distribution planning, inventorymanagement, allocation planning, order fulfilment, adjustment ofmanufacturing and inventory levels at various stocking points anddistribution centers, and/or one or more related tasks within supplychain network 100.

One or more supply chain entities 150 represent one or more supply chainnetworks, including one or more enterprises, such as, for examplenetworks of one or more suppliers 152, manufacturers 154, distributioncenters 156, retailers 158 (including brick and mortar and onlinestores), customers, and/or the like. Suppliers 152 may be any suitableentity that offers to sell or otherwise provides one or more items(i.e., materials, components, or products) to one or more manufacturers154. Suppliers 152 may comprise automated distribution systems 153 thatautomatically transport products to one or more manufacturers 154 based,at least in part, on one or more predicted supply chain events, one ormore predicted supply chain failures, one or more corrective actionsinitiated to resolve a predicted event or failure, one or more alerts,and/or one or more other factors described herein.

Manufacturers 154 may be any suitable entity that manufactures at leastone product. Manufacturers 154 may use one or more items during themanufacturing process to produce any manufactured, fabricated,assembled, or otherwise processed item, material, component, good, orproduct. In one embodiment, a product represents an item ready to besupplied to, for example, one or more supply chain entities 150 insupply chain network 100, such as retailers 158, an item that needsfurther processing, or any other item. Manufacturers 154 may, forexample, produce and sell a product to suppliers 152, othermanufacturers 154, distribution centers 156, retailers 158, a customer,or any other suitable person or entity. Manufacturers 154 may compriseautomated robotic production machinery 155 that produce products based,at least in part, on one or more predicted supply chain events, one ormore predicted supply chain failures, one or more corrective actionsinitiated to resolve a predicted event or failure, one or more alerts,and/or one or more other factors described herein.

Distribution centers 156 may be any suitable entity that offers to storeor otherwise distribute at least one product to one or more retailers158 and/or customers. Distribution centers 156 may, for example, receivea product from a first one or more supply chain entities 150 in supplychain network 100 and store and transport the product for a second oneor more supply chain entities 150. Distribution centers 156 may compriseautomated warehousing systems 157 that automatically remove productsfrom and place products into inventory based, at least in part, on oneor more predicted supply chain events, one or more predicted supplychain failures, one or more corrective actions initiated to resolve apredicted event or failure, one or more alerts, and/or one or more otherfactors described herein.

Retailers 158 may be any suitable entity that obtains one or moreproducts to sell to one or more customers. Retailers 158 may compriseany online or brick-and-mortar store, including stores with shelvingsystems 159. Shelving systems may comprise, for example, various racks,fixtures, brackets, notches, grooves, slots, or other attachment devicesfor fixing shelves in various configurations. These configurations maycomprise shelving with adjustable lengths, heights, and otherarrangements, which may be adjusted by an employee of retailers 158based on computer-generated instructions or automatically by machineryto place products in a desired location in retailers 158 and which maybe based, at least in part, on one or more predicted supply chainevents, one or more predicted supply chain failures, one or morecorrective actions initiated to resolve a predicted event or failure,one or more alerts, and/or one or more other factors described herein.Although one or more supply chain entities 150 are shown and describedas separate and distinct entities, the same entity may simultaneouslyact as any one of the one or more supply chain entities 150. Forexample, one or more supply chain entities 150 acting as a manufacturercan produce a product, and the same one or more supply chain entities150 can act as a supplier to supply an item to itself or another one ormore supply chain entities 150. Although one example of a supply chainnetwork 100 is shown and described, embodiments contemplate anyconfiguration of supply chain network 100, without departing from thescope described herein.

In one embodiment, visualization system 110 may be coupled with supplychain event predictor 120 using communication link 192 a and archivingsystem 130 using communication link 192 b, which may be any wireline,wireless, or other link suitable to support data communications betweenvisualization system 110, supply chain event predictor 120, andarchiving system 130 during operation of the system. Supply chain eventpredictor 120 may be coupled with visualization system 110 usingcommunication link 192 a and archiving system 130 using communicationlink 192 c, which may be any wireline, wireless, or other link suitableto support data communications between visualization system 110, supplychain event predictor 120, and archiving system 130 during operation ofthe system. Archiving system 130 may be coupled with network 190 usingcommunication link 194 a, which may be any wireline, wireless, or otherlink suitable to support data communications between archiving system130 and network 190 during operation of the system. One or more planningand execution systems 140 may be coupled with network 190 usingcommunication link 194 b, which may be any wireline, wireless, or otherlink suitable to support data communications between one or moreplanning and execution systems 140 and network 190 during operation ofthe system. One or more supply chain entities 150 may be coupled withnetwork 190 using communication link 194 c, which may be any wireline,wireless, or other link suitable to support data communications betweenone or more supply chain entities 150 and network 190 during operationof the system. Inventory system 160 may be coupled with network 190using communication link 194 d, which may be any wireline, wireless, orother link suitable to support data communications between inventorysystem 160 and network 190 during operation of the system.Transportation system 170 may be coupled with network 190 usingcommunication link 194 e, which may be any wireline, wireless, or otherlink suitable to support data communications between transportationsystem 170 and network 190 during operation of the system. Computer 180may be coupled with network 190 using communication link 194 f, whichmay be any wireline, wireless, or other link suitable to support datacommunications between computer 180 and network 190 during operation ofthe system.

Although communication links 194 a-194 f are shown as generally couplingarchiving system 130, one or more planning and execution systems 140,one or more supply chain entities 150, inventory system 160,transportation network 170, and computer 180 to network 190, any ofarchiving system 130, one or more planning and execution systems 140,one or more supply chain entities 150, inventory system 160,transportation network 170, and computer 180 may communicate directlywith each other, according to particular needs.

In another embodiment, network 190 includes the Internet and anyappropriate local area networks (LANs), metropolitan area networks(MANs), or wide area networks (WANs) coupling archiving system 130, oneor more planning and execution systems 140, one or more supply chainentities 150, inventory system 160, transportation network 170, andcomputer 180. For example, data may be maintained locally to, orexternally of, archiving system 130, one or more planning and executionsystems 140, one or more supply chain entities 150, inventory system160, transportation network 170, computer 180 and made available to oneor more associated users of archiving system 130, one or more planningand execution systems 140, one or more supply chain entities 150,inventory system 160, transportation network 170, and computer 180 usingnetwork 190 or in any other appropriate manner. For example, data may bemaintained in a cloud database at one or more locations external tovisualization system 110, supply chain event predictor 120, archivingsystem 130, one or more planning and execution systems 140, one or moresupply chain entities 150, inventory system 160, transportation network170, and computer 180 and made available to one or more associated usersof visualization system 110, supply chain event predictor 120, archivingsystem 130, one or more planning and execution systems 140, one or moresupply chain entities 150, inventory system 160, transportation network170, and computer 180 using the cloud or in any other appropriatemanner. Those skilled in the art will recognize that the completestructure and operation of network 190 and other components withinsupply chain network 100 are not depicted or described. Embodiments maybe employed in conjunction with known communications networks and othercomponents.

In accordance with the principles of embodiments described herein,visualization system 110 may generate alerts for predicted supply chainevents, such as an order promising failure or service level failure,that will occur during a current or future period but which will not beaccounted for in a supply chain plan. Based on the alert, a supply chainplanner accessing one or more planning and execution systems 140 mayinitiate an action to correct the supply chain event. Based on theselected corrective action, the one or more planning and executionsystems 140 may adjust forecasts, inventory levels at various stockingpoints, production of products of manufacturing equipment, proportionalor alternative sourcing of one or more supply chain entities, and theconfiguration and quantity of packaging and shipping of products andtaking into account the current inventory or production levels at one ormore supply chain entities 150. For example, the selected correctiveaction to resolve a supply chain failure associated with an alertgenerated by the visualization system 110 may comprise adding items toor removing items from a shipment of one or more supply chain entities150.

FIG. 2 illustrates visualization system 110 and supply chain eventpredictor 120 of FIG. 1 in more detail, in accordance with anembodiment. Visualization system 110 may comprise server 112 anddatabase 114, as discussed above. Although visualization system 110 isshown as comprising a single server 112 and a single database 114,embodiments contemplate any suitable number of servers or databasesinternal to or externally coupled with visualization system 110.

Server 112 of visualization system 110 comprises visualization module200 and contextual data retrieval module 202. Although server 112 isshown and described as comprising a single visualization module 200 anda single contextual data retrieval module 202, embodiments contemplateany suitable number or combination of these located at one or morelocations, local to, or remote from visualization system 110, such as onmultiple servers or computers at one or more locations in supply chainnetwork 100.

Database 114 of visualization system 110 may comprise one or moredatabases or other data storage arrangement at one or more locations,local to, or remote from, server 112. Database 114 of visualizationsystem 110 comprises, for example, supply chain event scores 210, alertdata 212, alert filters 214, and supply chain context data 216. Althoughdatabase 114 is shown and described as comprising supply chain eventscores 210, alert data 212, alert filters 214, and supply chain contextdata 216, embodiments contemplate any suitable number or combination ofthese, located at one or more locations, local to, or remote from,visualization system 110 according to particular needs.

In one embodiment, visualization module 200 of visualization system 110comprises a visualization user interface (UI), including a graphicaluser interface (GUI), that displays one or more interactivevisualizations including, for example, a heatmap showing the occurrencerisk of predicted supply chain events, production location of an itemassociated with the event, and distribution region for the item.Visualization module 200 may also generate and display visualisationsthat include graphs, charts, and other graphics illustrating supplychain metrics associated with the predicted supply chain events,probabilities that the supply chain events will occur, and theimportance of various factors in determining the occurrenceprobabilities. The visualizations are constructed from data receivedfrom the supply chain event predictor 120 and is updated with contextualdata from the contextual data retrieval module 202.

In one embodiment contextual data retrieval module 202 of visualizationsystem 110 retrieves contextual data for display in one or morevisualizations of master visualization dashboard 300 and stores theretrieved contextual data as supply chain context data 216 of database114. Contextual data retrieval module 202 identifies historical supplychain data associated with one or more features used by the supply chainevent predictor 120 to predict a supply chain event, (i.e. the values ofthe features increase or decrease the probability of occurrence ofpredicted supply chain events), retrieves the identified data fromvisualization system 110, supply chain event predictor 120, and/orarchiving system 130, and stores the data as supply chain context data216. In addition, contextual data retrieval module 202 may identify oneor more supply chain components associated with a predicted supply chainevent such as, for example, a SKU, a stocking location, one or moresupply chain entities 150, a distribution channel, a sales region, aresource, a material, or the like, and retrieve data associated with thepredicated supply chain event from one or more of database 114 ofvisualization system 110, database 124 of supply chain predictor 120,archive database 134 of archiving system 130, and/or one or more otherlocations local to or remote from supply chain network 100.Visualization module 200 processes the retrieved contextual data tocreate visualizations illustrating various aspects of the variables usedto make the supply chain event predictions and provide context of thepast, current, and future states of the supply chain network 100.

The various types of data stored in database 114 of visualization system110 will now be discussed.

Supply chain event scores 214 comprise the calculated probabilities thatone or more supply chain events will occur. Supply chain event predictor120 may calculate the probability that a particular supply chain eventwill occur using the processed data 230 and supply chain eventpredictions 232, as discussed in more detail below. Supply chain eventpredictor 120 analyzes values generated by the prediction problem andcalculates the probability that the target will be met based on theperformance of previous prediction values for predicting supply chainevents using that particular prediction model. When calculating theperformance of the model, supply chain event predictor 120 uses theperformance to update the occurrence probabilities for the predicatedsupply chain events. By way of example and not by way of limitation,visualization system 110 may calculate the probabilities of occurrencefor a supply chain event comprising a service level failure, such asthat caused by failing to meet a promised delivery time, by analyzingpast performance of calculations made during prediction phases of amachine learning process. The probability of occurrence for a supplychain failure may be termed a failure risk, as discussed in more detailbelow.

Alert data 216 comprises alerts generated by supply chain eventpredictor 120. According to embodiments, visualization system 110displays one alert element for each unique combination of item andstocking location that is predicted to cause a service level failureduring the prediction period. As described in more detail below, theitem-stocking location combination of the alert is used to create avisualization of alert elements that is ordered across one axis by SKUand across a second axis by a location. Although alerts are described asbeing generated for each unique item/stocking location combination thatis predicted to cause a service level failure, embodiments contemplatealerts generated for each SKU or other characteristic or combination ofcharacteristics of predicted supply chain events.

Alert filters 218 comprise a set of rules or, by way of example, a filewhich for each item location combination presents a list of alreadyknown facts which cause visualization system 110 to deprioritize or notdisplay alerts for one or more supply chain events based on the criteriaassociated with the selected filter. According to some embodiments,alert filters 218 removes alerts for one or more supply chain eventsthat are detected by one or more planning and execution systems 140 orwill be accounted for in a future planning session. By way of exampleand not of limitation, visualization system 110 may filter andautomatically hide alerts when, for example, the alerts are seen byother systems, alerts are for high manufacturing volumes of items,alerts indicate supply chain events that cannot be resolved because thecorrective lever does not exist, and the like.

Supply chain context data 220 comprises data retrieved by contextualdata retrieval module 212 of visualization system 110, as discussedabove. Supply chain context data 220 may include, for example, data thatincreases or decreases the probability of occurrence of one or moresupply chain events, data that supplements the supply chain eventpredictions, data illustrating various aspects of the variables used tomake the supply chain event predictions, data regarding one or morecomponents of a supply chain and which is associated with a supply chainevent, and/or data used to calculate any of the foregoing or generatedfrom any of the foregoing, including, for example, forecasts,calculations, or statistics generated from historical supply chain data.

Supply chain event predictor 120 may comprise server 122 and database124. Although supply chain event predictor 120 is shown as comprising asingle server 122 and a single database 124, embodiments contemplate anysuitable number of servers or databases internal to or externallycoupled with a supply chain event predictor 120.

Server 122 of supply chain event predictor 120 may comprise dataprocessing system 220 and prediction system 222. Although server 122 isshown and described as comprising a single data processing system 220and a single prediction system 222, embodiments contemplate any suitablenumber or combination of data processing system 220 and predictionsystem 222 located at one or more locations, local to, or remote fromsupply chain event predictor 120.

Database 124 of supply chain event predictor 120 may comprise one ormore databases or other data storage arrangement at one or morelocations, local to, or remote from, server 122. Database 124 comprises,for example, processed data 230, supply chain event predictions 232, andprediction models 234. Although, database 124 is shown and described ascomprising processed data 230, supply chain event predictions 232, andprediction models 234, embodiments contemplate any suitable number orcombination of these, located at one or more locations, local to, orremote from, supply chain event predictor 120 according to particularneeds.

In one embodiment, data processing system 220 of supply chain eventpredictor 120 comprises one or more modules that receive historicalsupply chain data from archiving system 130 and prepares the data for aprediction problem, such as, for example, machine learning. The data isprepared for a prediction problem by, for example, aggregating the dataat a particular granularity level, normalized to be quantity independent(wherein the supply chain data has the same meaning independent of thequantity of an item sold), and then rescaled to compare data ofdifferent scopes. According to embodiments, data processing system 220transforms historical supply chain data by one or more rescaling,normalization, and/or transformation process to, for example, aggregateone or more variables (inventory level, forecast, logistics delays,production quantity, etc.) at the same granularity level; determiningall actual, current, or past quantities in terms of a ratio of theoriginal quantity divided by the plan quantity, computation of quantityindependent key performance indicators (such as, for example, pastservice level in % per week), and the like.

Prediction system 222 may predict the supply chain event by modeling theoccurrence of the supply chain event as a prediction problem using oneor more prediction models 234. In one embodiment, the supply chainprediction problem may comprise a type of classification problem thatpredicts whether the service level will be above a target set by one ormore supply chain entities 150. Prediction system 220 may solve theprediction problem comprising a classification problem using a suitablemachine learning model.

In embodiments comprising a machine learning model, prediction system220 receives samples of transformed historical supply chain dataaggregated at a certain granularity level during a training phase. Thegranularity level may comprise, for example, data aggregated by item,stocking location, and time period for one or more planning periods,such as, for example, data aggregated by SKU-week, for a planning periodof four-weeks. The transformed historical data aggregated at theitem-stocking location-week level for the four-week planning periodcomprises a sample to train the supply chain event predictor 120 system,which is then presented with several snapshots of this data andpresented with a prediction problem requiring predicting service levelfailures for a particular forward looking period. For example, the dataprocessing and prediction system 220 may be trained by receiving one ormore years of archived data, presented in four-week snapshots andrequested to predict the service level failures in the three weeks afterthe time period covered by the snapshot. After training the machinelearning model, prediction system 220 then predicts future service levelfailures for three weeks when presented with snapshots of four-weeksamples of supply chain data. According to some embodiments, theprediction phase of the machine learning process is performed at weeklyintervals. However, embodiments contemplate shorter prediction phasesthat may be performed, for example, twice a week, once a day, or thelike. In addition, prediction system 222 predicts supply chain eventsthat occur only within a prediction horizon. According to embodiments,the prediction horizon comprises a length of time long enough for one ormore supply chain entities 150 to enact a corrective action for thepredicted supply chain event and shorter than the planning horizon.According to embodiments, prediction system 220 sets the predictionhorizon to a time period shorter than a time period when supply chainevents are predicted to happen during a planning period that will becaptured and corrected by a planning system.

The various types of data stored in database 124 of supply chain eventpredictor 120 will now be discussed.

Processed data 230 comprises historical supply chain data processed bydata processing system 220 of supply chain event predictor 120 for useby prediction system 222. The processed data may comprise data checkedby data processing system 220 for correct range, sign, or value.Processed data 230 may also comprise statistics generated from the data.Additionally, processed data 230 may be transformed to adjust the scopeto a certain granularity level, normalized to be quantity independent,where the supply chain data has the same meaning independent of thequantity of an item sold, and then rescaled to compare data of differentscopes.

Supply chain event predictions 232 comprise events predicated to occurduring a current or future time period when the value of a supply chainmetric will not meet a selected targeted value. By way of example andnot of limitation, a supply chain event may comprise an order promisingfailure that occurs when a business is unable to deliver an item withina promised time period.

Prediction models 234 may comprise machine learning models (including,for example, a gradient boosted trees machine learning model, a neuralnetwork model, and the like).

FIGS. 3A-F illustrate master visualization dashboard 300, according toan embodiment. As discussed above, visualization module 210 displaysalerts indicating whether a supply chain event (such as, for example, aservice level failure) will occur during a prediction period. Accordingto embodiments, master visualization dashboard 300 displays predictionsof supply chain events using one or more visualisations that emphasizesupply chain events with the largest impacts or the highest probabilityof occurring. Additionally, master visualization dashboard 300 providescontextual information that automatically updates to provide the stateof one or more supply chain entities 150 associated with one or moresupply chain events, which provides a centralized system for predictingand determining causes for various supply chain events without anydirect interface with supply chain planning and execution systems 140.

According to embodiments, master visualization dashboard 300 comprisesalert prediction overview 400 (FIGS. 4A-B), alert priority visualizer600 (FIG. 6 ), selected alert overview 700 (FIG. 7 ), location overviewvisualization 1000 (FIG. 10 ), production overview visualization 1100(FIG. 11 ), and logistics overview visualization 1200 (FIGS. 12A-B), asdescribed in greater detail below.

According to embodiments, master visualization dashboard 300 displaysalerts at both alert predication overview 400 and alert priorityvisualizer 600. As described in more detail below, visualization module200 display alerts for supply chain events using only historical supplychain data. In a typical supply chain network, a production managerwould receive alerts from the production system with rules developed inthe production system, and logistics manager would receive alerts fromthe transportation management systems with rules developed in thetransportation management system. However, visualization system 110generates a master visualization dashboard 300 that predicts bothproduction system alerts and transportation management system alerts foran entire supply chain network 100 in a single location, withoutreal-time data or access to the production system rules or thetransportation management system rules.

FIGS. 4A-B illustrate the supply chain alert prediction overview 400 ofFIGS. 3A-F in more detail, in accordance with an embodiment. Supplychain alert prediction overview 400 comprises alert heatmap 402, modelperformance visualization 404, alert filter selector 406, and modelversion selector 408. Visualization system 110 generates an alertheatmap 402 comprising alert elements 410 that represent predictedsupply chain events categorized by each unique combination of itemidentifier and stocking location and color-coded to indicate theprobability that the predicted supply chain event will occur.

In addition, visualization module 200 may display the performance of theprediction model as model performance visualization 404. According toembodiments, model performance visualization 404 displays theperformance of the model for a selected alert by plotting thecorresponding precision and recall of the selected alert on aperformance curve 412 and displaying the numerical values for theprecision and recall measurements, which are used to calculate theoccurrence probability for predicted supply chain events, the failurerisk for predicted supply chain failures, and/or one or more othermetrics described herein.

Alert filter selector 406 comprises an interactive element of thevisualization UI providing for selection or input of filters that causevisualization system 110 to not display one or more alerts based on thecriteria of the selected filter. Visualization system 110 displaysselected active filters 414 in alert filter selector 406. One or moreselected active filters 414 comprise filters currently being used byvisualization system 110 to prioritize, hide, or display one or morealert elements 410 based on criteria in one or more selected activefilters 414 and properties of the alerts represented by the alertelements 410, including, for example, the supply chain event, item, SKU,stocking location, one or more supply chain entities 150, time period,occurrence risk, or other property or value associated with the one ormore alerts represented by the one or more alert elements 410.

Model version selector 408 comprises an interactive element of thevisualization UI providing for selection of predictions from one or moredifferent time periods. For example, model version selector 408 providesfor displaying predictions from a previous week, from two weeks prior, aprior month, year, or the like. Although model version selector 408 isdescribed as providing selection and display of predictions fromparticular time periods, embodiments contemplate selection and displayof predictions from a particular hour, day, week, month, planningperiod, season, year, or any suitable time period.

According to some embodiments, alert heatmap 402 comprises a firstdimension 420 representing stocking locations at one or more supplychain entities 150, a second dimension 422 representing one or moreitems that are stocked at the stocking locations of the one or moresupply chain entities 150, and third dimension 424 representing anoccurrence risk of a supply chain event. According to embodiments,visualization system 110 displays one or more alert elements 410 at theintersection of first dimension 420 comprising a stocking location andsecond dimension 422 comprising item identity based on the stockinglocation and item of the supply chain event of the alert represented byeach of the one or more alert elements 410. Visualization system 110 mayrepresent third dimension 424 by displaying different types of one ormore alert elements 410, wherein each type of the one or more alertelement 410 is associated with a different value or ranges of values ofthe occurrence risk of the supply chain event associated with the alertrepresented by each of the one or more alert elements 410. For example,visualization system 110 may display one or more alert elements 410 withdifferent colors, wherein each of the different color represents a valueor range of values of the occurrence risk.

By way of example and not by way of limitation, when the colors of theone or more alert elements 410 is used to indicate the occurrence riskof the supply chain event alert represented by each of the one or morealert elements 410, visualization system 110 may display alert elements410 representing alerts for supply chain events with a high occurrencerisk a red color, alert elements 410 representing alerts for supplychain events with a low occurrence risk a green color, and alertelements 410 representing alerts with intermediate occurrences risks arange of orange and yellow colors. Additionally, visualization system110 may display alert elements 410 using a black color or white color atintersections of first dimension 420 and second dimension 422 that donot represent actual combinations of stocking locations and items.Visualization system 110 may display alert elements 410 for which noprediction has been made using a black color, a white color, or othercolor. In addition or in the alternative, visualization system 110 maynot display alert elements 410 at intersections of first dimension 420and second dimension 422 that do not represent actual combinations ofstocking locations and items or for which no prediction has been made.Although alert elements 410 are described as comprising particularcolors to indicate certain values or ranges of values of occurrencerisks, embodiments contemplate alert elements 410 comprising anysuitable color to indicate particular values or ranges of values for oneor more other metrics of the supply chain events represented by thealerts, including, for example, probabilities of occurrence, failurerisk, or the like.

The exemplary manufacturer discussed above comprises predictions fororder promising failures for each SKU-distribution center combination inthe supply chain. In the illustrated embodiment comprising the exemplarymanufacturer, first dimension 420 comprises an x-axis representingitems, and second dimension 422 comprises a y-axis representing thedistribution centers which stock each of the SKUs prior to shipment tofulfill customer orders. According to this example, for existing SKUsthe color of the alerts of alert heatmap 402 indicates the system'sprediction of a future service level failure, wherein red represents ahigh risk of failure, green represents a low or no risk of failure, theone or more yellow and orange colors transitioning from red to greenrepresent intermediate risks of failure, and black represents itemlocation combinations that are not predicted for the displayed week.

When alert elements 410 are grouped into a column or row and every alertelement 410 in the column or row is associated with one of one or moresupply chain entities 150, such as, for example, the manufacturingplant, a distribution center, or the like, heatmap 402 provides thevisualization of global or wide-spread problems, which are indicatedwhen all or nearly all alert elements 410 in a row or a column indicatethat a supply chain event is likely to occur, such as, for example, byall of the alert elements 410 displayed with a red color. Continuingwith the example of the exemplary manufacturer, each SKU is produced atonly one of the one or more supply chain entities 150 and all alertelements 410 in a single column represent one item, when adjoiningcolumns of alert elements 410 representing the same plant are displayedwith a red color, visualization system 110 indicates the likely cause isa production problem that affects all of the items at a manufacturingfacility. Additionally, in the illustrated embodiment of the exemplarymanufacturer, all alert elements 410 in a single row representdistribution centers, which are grouped according to geographic region(e.g. Central Europe (EUC), Northern Europe (EUN), Western Europe (EUO),and Southern Europe (EUS)). When an entire row of alert elements 410 isdisplayed with a red color (or other graphic representing a high risk ofoccurrence for the supply chain event), visualization system 110indicates the likely cause is likely a logistics problem that affectsall of the items at the distribution center, and when adjoining rows ofalert elements 410 grouped into the same geography region are displayedwith a red color (or other graphic representing a high risk ofoccurrence for the supply chain event), visualization system 110indicates the likely cause is likely a regional problem.

FIGS. 5A-B illustrate the interactivity of one or more alert elements410 of FIGS. 4A-B in more detail, in accordance with an embodiment.According to some embodiments, one or more alert elements 410 compriseinteractive display regions of a GUI that are selectable by, forexample, clicking on or hovering over the display region comprising oneor more alert elements 410 using a mouse, touchscreen, or other inputdevice. Selected alert element 502 comprises one of the one or morealert elements 410 having an interactive display region and selectedusing the visualization UI of visualization module 200. When one or morealert elements 410 are selected, visualization module 200 displays oneor more visualizations comprising information related to selected alertelement 502 on the master visualization dashboard 300. According to someembodiments, visualization module 200 displays, in response to selectionof one of the one or more alert elements 410, one or more visualizationthat, for example, highlight selected alert element 502 on alert heatmap402, updates the visualizations of the master visualization dashboard300 to display visualizations associated with the supply chain event ofthe alert represented by selected alert element 502, display iteminformation popup 504, and/or display occurrence risk popup 506. Iteminformation popup 504 comprises a graphical element displayed byvisualization module 200 and identifying the item associated with thesupply chain event for the alert represented by the selected element502. Occurrence risk popup 506 comprises a graphical element displayedby visualization module 200 and identifying, for example, an occurrencerisk of the supply chain event represented by the selected alert 502, analert identification number, and the regional distribution centerassociated with the supply chain event for the alert represented by theselected alert 502.

For the illustrated exemplary manufacturer, alert elements 410correspond to alerts representing predicted service level failures forvarious products being distributed from European distribution centers.Continuing with this example, when one of the one or more alert elements410 is selected, visualization module 200 updates one or morevisualizations of the master visualization dashboard 300 to displaycontextual information related to the predicated service level failurerepresented by the alert corresponding to selected alert element 502,such as, for example, inventory and orders for the item associated withthe selected alert element 410.

In addition and as described above, visualization module 200 may displaythe performance of the prediction model in model performancevisualization 404. According to embodiments, when one or more alertelements 410 are selected, visualization module 200 displays theperformance of the model for selected alert 502 by displaying thecorresponding precision and recall of selected alert 502 on performancecurve 412 using indicator 508 and displaying numerical values for theprecision and recall measurements with overlay 510. Precision and recallmeasurements indicate the performance of the prediction model and areused to calculate the occurrence probability for predicted supply chainevents, the failure risk for predicated supply chain failures, and/orone or more other metrics, as described herein.

Supply chain event predictor 120 predicts the occurrence of supply chainevents, such as, for example, an order promising failure, by training aprediction model using historical supply chain data. Over time,historical supply chain data may grow to include additional historicalsupply chain data from newer time periods. Supply chain event predictor120 may use the newer historical supply chain data to train theprediction model, which may improve the performance of the predictionmodel and the quality of the predictions. After training, supply chainevent predictor 120 may test the performance of the updated predictionmodel by generating predictions of supply chain events over a particulartime period. Some of the predicted supply chain events will correspondto supply chain events that actually occur (i.e. the prediction iscorrect) while other predicted supply chain events will not occur (i.e.the prediction is wrong). Additionally, supply chain event predictor 120will fail to predict some supply chain events that actually occur (i.e.the supply chain event is missed). Using these measurements of thenumber of correct predictions, total predictions, and total actualsupply chain events, the performance of the supply chain model may becalculated using two measurements, precision and recall. Precision is ameasurement of the number of correct positive predictions divided by thetotal number of positive predictions. For example, when supply chainevent predictor 120 uses the model to predict service level failures ina supply chain network for a three-week period, precision is a measureof the number of predicted service level failures that actually occur inthe supply chain network during the three-week period divided by thetotal number of predictions of failure. A precision of 80% wouldindicate that, for eight out of ten predictions, the predicted event(such as, a service level failure) will actually occur.

Recall is the measure of correct predictions divided by the total numberof actual supply chain events. For example, when supply chain eventpredictor 120 uses the model to predict service level failures, recallis a measure of the number of predicted service level failures thatactually occur divided by the total number of service level failures(i.e. both the predicted service level failures and the non-predictedservice level failures) that actually occur in supply chain network 100for the three week period. A recall of 80% indicates that eight out often supply events (such as, a service level failure) that occur will bepredicted.

When the model has a high recall, the precision is generally lower andthe visualization module 110 will display predictions for a largernumber of the supply chain events that occur but will also display alarge number of predictions that are not correct. On the other hand,when the model has a high precision, the recall is generally lower andthe visualization module 110 will display a fewer number of predictionsthat are globally more correct, but it will fail to predict a largenumber of the total supply chain events. According to embodiments, eachsupply chain event prediction is associated with a precision and recallvalue, given the past performance of the model in similar context.Although only a single model is illustrated and described, embodimentscontemplate more than one model, wherein various models may havedifferent curves of precision and recall.

According to embodiments of the prediction model comprising a machinelearning model, each time the machine learning model is trained, theprecision and recall values of performance curve 412 are measured andthe visualization module 200 updates performance curve 412 with thenewly measured values. As discussed above, when one or more alertelements 410 are selected, visualization system 110 displays precisionvalue and recall value on overlay 510 which indicates the pastperformance of the prediction model for selected alert 502, whichindicates the confidence that the supply chain event predicted byselected alert 502 will occur or that a supply chain event will bemissed.

FIG. 6 illustrates the supply chain alert priority visualizer 600 ofFIGS. 3A-F in more detail, in accordance with an embodiment. Supplychain alert priority visualizer 600 displays alerts for predicted supplychain events as bars 602 in a column, ordered according toprioritization or filter criteria, and colored, such as, for example,red, orange, yellow, and green) to indicate occurrence risk, value ofsorting criteria, precision value, or the like. Filter selection box 604comprises dropdown selection boxes providing for filtering bars 602 todisplay alerts according to item, stocking location, and manufacturingplan. Although particular filtering criteria are illustrated,embodiments contemplate other filtering criteria, according toparticular needs. According to embodiments, when one or moreprioritizations or filters are selected, visualization module 200displays bars 602 in accordance with the criteria of the prioritizationor filter. In an embodiment where alerts represent service levelfailures, bars 602 may be ordered according to the failure risk of theservice level failure of the associated alert.

In addition, embodiments contemplate automatic hiding and filtering ofalerts that, for example, will be accounted for in the planning processof the supply chain network 100, represent high manufacturing volumes ofitems, represent null safety stock, and the like. When the supply chainevent predictor 120 uses a machine learning model, the system maygenerate alerts for situations that are already handled by the one ormore planning systems and will be resolved elsewhere in the supply chainnetwork 100. For example, when an item is discontinued or a distributioncenter is closed, visualization system 110 may display an alert thatstock is lacking at one or more supply chain locations. However,according to some embodiments, visualization system 110 may be set toautomatically filter and not display any alerts for discontinued itemsor closed distribution centers or other supply chain entities 150.Embodiments contemplate, filters for displaying alerts for strategictires or strategic distribution centers with a higher importance orpriority than alerts for non-strategic tires or non-strategicdistributions centers. Similarly, one or more filters may cause alertsfor products with high sales volumes or for priority customers bedisplayed prior to alerts for products with low sales volumes or fornon-priority customers, even if the products with low sales volumeproducts or for non-priority customers have a higher failure risk.

By way of a further example, a manufacturer may have a productioncapacity problem where total orders exceed supply, but all plants arerunning at maximum capacity. Visualization system 110 may automaticallyfilter and not display the alert for the production capacity problembecause the only resolution would be to build additional manufacturingcapacity, which is not an actionable event because building additionalmanufacturing plants may take several years. In other words,visualization system 110 may automatically not display alerts using oneor more exclusion rules (which may be user-supplied exclusion rules) forpredicted events where the only resolution is not actionable within apractical horizon.

Another type of alert filtering comprises deprioritizing or notdisplaying alerts that are handled by other supply chain planningsystems. In one embodiment, visualization system 110 does not display analert for a service level failure caused by a production planningproblem when the production planner will account for the service levelfailure in the production forecast. In addition, according to someembodiments, when a production planning system will identify whenpredicted stock is not projected to meet the safety stock, visualizationsystem 110 may filter and not display an alert for a correspondingpredicated supply chain event.

FIG. 7 illustrates the alert visualization of FIGS. 3A-F in more detail,in accordance with an embodiment. According to embodiments, alertvisualization 700 comprises alert identification 702, alert summary 704,occurrence risk scoreboard 706, features contributions visualization708, and contextual data scoreboard 710. Alert identification 702comprises an identifying code or text that identifies one or more supplychain components associated with the alert, such as, for example, thename of the item, a SKU, a stocking location, a manufacturer, adistribution region, or any other supply chain component, including theidentity of one or more supply chain entities associated with the alert.For example, the illustrated selected alert identification 702 comprisesan alert identifier represented by a three-digit number, a SKUrepresented by a six-digit number and a three-digit number connected byan underscore mark, and a distribution center identifier comprising athree digit alphabetic code.

According to embodiments, alert summary 704 comprises a description ofthe item, the region where the item is distributed, the seasonality ofthe item, the lead time between the item's stocking location and theitem's the manufacturing plant, and the like. Although alert summary 704is described as comprising particular description of an item,embodiments contemplate alert summary 704 comprising any suitabledescriptive elements, including, for example, pictures, sales andproduction volume, and the like, according to particular needs.

According to embodiments, occurrence risk scoreboard 706 comprises ametric for assessing the probability that the predicted supply chainevent will actually occur, such as, for example, an occurrence risk,failure risk, or the like. According embodiments, occurrence riskscoreboard 706 comprises a failure risk score, which indicates a scoreof the probability that a predicted service level failure will result inan actual service level failure. As described in more detail below,visualization system 110 displays a score of the occurrence risk, whichis calculated by visualization system 110 and/or supply chain eventpredictor 120 based on one or more factors identified during training ofthe prediction model and which are determined to have predictive powerwith regard to the occurrence of a particular supply chain event using,for example, precision/recall or receiver operating characteristics. Forexample, occurrence risk scoreboard 706 comprises an occurrence riskscore of 74% and indicates which predictive factor contributed thelargest amount to the occurrence risk score, such as, for example, “PastService Level.” This indicates that supply chain event predictor 120calculated a score of the probability that a service level failure willoccur for this predicted supply chain event with 74% probability basedon one or more predictive factors identified during training of theprediction model, of which “Past Service Level” was the predictivefactor with the highest contribution to the score.

According to embodiments, features contributions visualization 708comprises a visualization of the contribution from one or morepredictive factors 720-728 to the overall precision score of thesnapshot. According to embodiments comprising an exemplary machinelearning prediction model, supply chain event predictor 120 identifiesfrom historical supply chain data, prediction features 720-738, whichare data or data features associated with an increased or decreasedprobability of occurrence of a supply chain failure, such as, forexample, a service level failure. According to embodiments, predictionfeatures may comprise past service level feature 720, stock feature 722,fill rate feature 724, plant stock/safety stock feature 726, andproduction gaps feature 728. Although particular features areillustrated and described, embodiments contemplate any one or morepredictive factors, according to particular needs.

When supply chain event predictor 120 uses the trained machine learningmodel to assess samples of supply chain data to identify predictedsupply chain failures, supply chain event predictor 120 generates amodel prediction 730 comprising a value between 0 and 1, where thecloser the value is to 1, the more likely a failure will occur. Toderive a probability of failure or failure risk score, prediction system222 analyzes the past performance of model prediction 730. For example,during a training phase of machine learning, prediction system 222 mayanalyze a probability of failure for a particular model prediction.Assuming, for example, model prediction 730 is 0.8, and 50% of thesample having a model prediction greater than 0.8 correctly predictedfailures, the probability of failure, also called precision may bemeasured as 50% when operating at a model prediction of 0.8. Thecalculated probability of a failure occurring is a failure risk score,as described above. In addition, visualization system 110 may generateand display a visualization of the contribution of each predictionfeature to the probability that an alert will result in a service levelfailure, which may be used to identify which supply chain systems needto be adjusted to avoid the service level failure.

Contextual data scoreboard 710 comprises a visualizations comprising oneor more visual elements that represent historical supply chain dataand/or contextual data to illustrate important measurements, KPIs, orcharacteristics of one or more supply chain planning and executionsystems 140 and/or one or more supply chain entities 150 that guide theselection of one or more corrective actions to resolve a supply chainevent of the alert represented by selected alert element 502. Accordingto embodiments, contextual data scoreboard 710 comprises historical itemservice level score, which represents the service level for one or morehistorical time periods of a SKU associated with the supply chain eventof the alert represented by selected alert element 502. According to oneembodiment, visualization system 110 and/or supply chain event predictor120 calculates the historic SKU service level for one or more previoustime periods and displays the result using a numerical score 740 andannular visualization 742. In the illustrated example comprising anexemplary manufacturer, numerical score 740 indicates that the historicservice level is 44%, which indicates 44% of orders by volume of productwere successfully filled on time, while nearly 56% were not successfullyfilled on time. In this manner, the performance of the item across alllocations can be quickly compared with its performance at the locationassociated with the SKU of the service level failure of the selectedalert. When an item is performing much better at the location of theselected alert than the global performance at all locations, then theproblem causing the alert is likely not caused by the current location.On the other hand, when the item's performance at the alert location isworse than the global item performance, then the underlying causes ofthe alert is more likely associated with the alert location.

Furthermore, in some embodiments, contextual data scoreboard 710comprises an arrow 770 representing the trend of the service level overthe course of the last few weeks. For example, the arrow of theexemplary alert visualization is pointing up in the illustratedembodiments, which indicates the service level of the item has beenimproving in the last two weeks.

FIG. 8 illustrates features contributions visualization 708 of FIG. 7 inmore detail, in accordance with an embodiment. As described above,features contributions visualization 708 displays the contribution fromone or more prediction features 720-728 to the overall precision scoreof the snapshot. One or more prediction features 720-728 comprisemeasured values from archived supply chain data and/or one or moremachine learning features derived from archived supply chain data.According to an embodiment, features contributions visualization 708comprises a waterfall chart where each of the first five bars representsthe contribution of a prediction feature to the calculated value of theprecision value, which is represented by the sixth bar and is correlatedto occurrence risk score 710. As described in more detail below,features contributions visualization 708, according to a waterfall chartembodiment, identifies the magnitude and direction of the effect of eachof the one or more prediction features 720-728 by the color and size ofthe bars 802 a-802 e. Bars 802 c-802 d having a first color (e.g. green)may represent a positive impact to the precision level while bars of asecond color 802 a-802 b and 802 e (e.g. red) may represent a negativeimpact to the precision level. As discussed above, final bar 804 of thewaterfall chart may represent a precision level and comprise thecumulative sum of the contributions of each of prediction features720-728. By visualizing prediction features 720-728 in this manner,visualization system 110 displays the context of an alert and thefactors that contribute to the failure risk, which suggests the one ormore corrective actions to prevent the supply chain event. However, asdiscussed below, one or more features 720-728 and the contribution ofthe one or more prediction features 720-728 to an overall scorerepresenting the probability that a failure will occur may be visualizedin various different configurations.

In the illustrated example, final bar 804 of features contributionsvisualization 708 indicates the model prediction is 0.502. Based on theprecision and recall measurements calculated for the prediction model,an overall prediction of 0.502 was determined to correspond to aprobability of occurrence of 74%, which may be referred to as anoccurrence risk. For a service level failure, the occurrence risk may bereferred to as a failure risk. Each of the bars 802 a-802 e illustratesthe contributions from one or more features 720-728 to the probabilitythe supply chain event will occur. For example, the various contributingfactors to the overall prediction may comprise the service level over apreceding time period (here, three weeks), the stock at the distributioncenter compared with safety stock levels over a preceding time period(here, again a three week period was selected), the distribution centerfill rate, the stock of the SKU at the manufacturing plant versus thesafety stock, and the amount of product in the production plan versusthe needs (i.e. production gaps).

To further explain, an example is now given in relation to the exemplarymanufacturer. Continuing with this example, first bar 802 a representsservice level feature 720 comprising the service levels in the lastthree weeks. In this case, the particular product may not be supplied tothe particular distribution center because the product failed to besupplied correctly from a distribution center for the last three weeks.This may then be a factor that this product will not be supplied on timeduring the displayed time period. Second bar 802 b represents stockfeature 722 comprising how much stock is at the distribution centercompared with the safety stock for the last three weeks. Whether theprojected safety stock is going up or down can give some insight intothe stock at the distribution center stock over the next few weeks.Third bar 802 c represents fill rate feature 724 comprising the fillrate of the distribution center. In other words, for all of thedifferent products combined, when the distribution center is filled withnear 100% of capacity, there may be some logistic delay which would be afactor that would increase the overall service level failure risk. Forexample, when trucks have been late for three weeks in a row, it mayimpact the future service level. Fourth bar 802 d represents plantstock/safety stock feature comprising how much stock is at themanufacturing plant compared with the safety stock for the last threeweeks. Fifth bar 802 e represents production gaps feature 728 comprisingthe production plan compared with the predicted demand. In other words,what is the difference between what is being planned to be produced andwhat are the actual needs of the supply chain. Finally, and as describedabove, bars 802 a-802 e represent each of prediction features 720-728that feed into the overall occurrence risk score which is calculatedbased on precision value 730 represented by final bar 804. Althoughparticular factors are illustrated, embodiments contemplate othercombinations of one or more features 720-728 used to predict theoccurrence of a supply chain failure, according to particular needs.

FIG. 9 illustrates temporal prediction contributions visualization 900in more detail, in accordance with an embodiment. According toembodiments, temporal prediction contributions visualization 900displays the direction of the contribution from each of five features720-728 to the overall failure risk for one or more time periods. Forexample, temporal prediction contributions visualization 900 comprisesthree historical time periods 902 a-902 c (to the left of the verticalline), a current time period 902 d indicated by a vertical line, andthree future time periods 902 e-902 g (to the right of the verticalline.) Circular icons 904 are located in five rows at each of the seventime periods 902 a-902 g, where each row indicates one of five features720-728 used to calculate an occurrence risk score and the color of thecircular icon indicates whether one or more features 720-728 gives apositive, negative, or neutral contribution to the alert risk score forthe time period 902 a-902 g and one or more features 720-728 associatedwith its location. For example, temporal prediction contributionsvisualization 900 indicates a positive contribution using a greencircle, a negative contribution using a red circle, and a neutralcontribution using a black circle. Using this scheme, the temporalprediction contributions visualization 900 identifies that fill ratefeature 724 and plant stock/safety stock feature 726 both contributedpositively in historical time periods 902 a-902 c, while past servicelevel feature 720 has increased the risk of supply chain failure in thecurrent time period 902 d, and stock feature 722 is predicted toincrease the risk of a supply chain failure in all three futureprediction time periods 902 e-902 g.

FIG. 10 illustrates the location overview visualization 1000 of FIGS.3A-F in more detail, in accordance with an embodiment. According toembodiments, location overview visualization 1000 comprises summariesfor one or more items and locations associated with the selected alertfor three historical time periods 1002 a-1002 c (to the left of thevertical line), a current time period 1002 d indicated by a verticalline, and three future time periods 1002 e-1002 g (to the right of thevertical line.) According to embodiments, an orders and stockvisualization 1004 of location overview visualization 1000 displayssummaries of safety stock levels 1010, available-to-promise (ATP) levels1012, stock levels 1014, future orders 1020, orders where service levelwas not met (Tires Service Level: 0) 1022, and orders where serviceslevel was met (Tires Service Level: 1). Additionally, cumulative ordersand forecasting visualization 1006 of location overview visualizations1000 may comprise confirmed orders 1030, summaries of cumulativeforecasting gaps with confirmed orders including, for example, acomparison of a previous month's forecast with previous forecast (LAG 1)1032, and comparisons of forecasts with orders (Prev CT) 1034 such as,for example, illustration of under-forecasting and over-forecasting, fora current period 1002 d and eight future time periods 1002 e-1002 l.

According to embodiments, location overview visualization 1000 displaysthe cumulative production forecast for the last monthly forecast createdby the planning and execution systems for a particular SKU. Cumulativeorders and forecasting visualization 1006 comprises a cumulative orderthat has been made so far. A vertical line at the current period 1002 dindicates the demarcation between historically sourced metrics andpredicted. Cumulative orders and forecasting visualization 1006illustrates that approximately forty items would sell at a future timeperiod 1002 f and that the last forecast predicted an even higher numberof sales in anticipation of the increased sales. Even with the increasedforecast, cumulative orders and forecasting visualization 1006 indicatesthat even with the increased forecast, the system still under-forecastedsales, and the order was not fully filled. The under-forecast for theorder caused the alert as indicated by the contextual data of theproduct overview visualization 1100. Based on the contextual supplychain data, the corrections to resolve the supply chain event thatcaused the alert may include, for example, manually increasing the itemstock or manually increasing the forecasts.

FIG. 11 illustrates the production overview visualization 1100 of FIGS.3A-F in more detail, in accordance with an embodiment. According toembodiments, production overview visualization 1100 comprises summariesof production for one or more products at a manufacturing plant for fourhistorical time periods 1102 a-1102 d (to the left of the verticalline), a current time period 1102 e indicated by the vertical line, andfour future time periods 1102 f-1102 i (to the right of the verticalline.) According to embodiments, the production overview visualizations1100 illustrate summaries of production for one or more products, suchas, for example, the current product stock at a particular manufacturingplant, using production plant visualization 1104. Production plantvisualization 1104 comprises current stock at plant 1112, currentproduction needs 1110, planned production 1114, actual production 1116,and metrics comparing production with needs 1118-1120. Needs priorityvisualization 1106 illustrates the total needs for an item 1130, andwhether the items are needed for export 1132 or particular markets(RT_A, RT_B, and RT_C) or other destinations. Production overviewvisualizations 1100 display contextual data for production problemsassociated with a selected alert, such as when one or more items arestuck at a manufacturing plant, what portion of a production plan wasexecuted, and what percentage of total products in the plan wereactually produced.

FIGS. 12A-B illustrate logistics overview visualization 1200 of FIGS.3A-F in more detail, in accordance with an embodiment. According toembodiments, logistic overview visualizations 1200 comprise, forexample, stock movement visualizations 1202-1206, such as total volumeentering a location every week 1202, and total volume in the locationevery week 1204, total volume leaving the location every week 1206) andlogistic delays visualization 1210 illustrating transportation delaysfor a product between a source and a location. Stock movementvisualizations 1202-1206 illustrate for a current time period 1220, oneor more past time periods, and one or more future time periods, capacity1230, 80% capacity 1232, and 120% capacity 1234 of the distributioncenter COS. Total volume entering a location every week visualization1202 and total volume leaving the location every week visualization 1206may additionally display movement of total volume of goods (flux) 1240,80-120% of flux 1242, and greater than 120% of flux (120%+) 1244. Totalvolume in the location every week visualization 1204 may additionallydisplay total volume of stock 1250. Logistic delays visualization 1210displays delays in tire shipments for volumes of tires delivered and notdelivered between a manufacturing plant U01 and a distribution centerVIT including, for example, Delivery 3+ days late 1260, Delivery 2 dayslate 1262, Delivery 1 day late 1264, Delivery on time 1266, Notdelivered 3 days late 1268, Not delivered 2 days late 1270, Notdelivered 1 day late 1272, Not delivered on-time 1274, and average delayin the last week between the manufacturing plant and the distributioncenter 1280, average for the manufacturing plant and all distributioncenters 1282, and average for the distribution center and allmanufacturing plants 1284.

Continuing with the example of the exemplary manufacturer, the logisticsproblem visualizations 1200 show capacity for what is entering at thedistribution center, what is leaving the distribution center, the globalstock, and the like. This may illustrate how much latitude a supplychain planner may have to implement a correction to a predicted supplychain failure by visualizing the amount of available storage for an itemat a particular distribution center. For example, when a distributioncenter is full, a correction that adds more stock to the distributioncenter may not resolve the supply chain failure and, instead, may causeone or more additional failures because the distribution center cannothandle any more stock. In addition, the visualization may illustrate theaverage delays in product over the last few weeks, which shows if therehave been logistic problems recently and if future orders will be madeon time.

Reference in the foregoing specification to “one embodiment”, “anembodiment”, or “some embodiments” means that a particular feature,structure, or characteristic described in connection with the embodimentis included in at least one embodiment of the invention. The appearancesof the phrase “in one embodiment” in various places in the specificationare not necessarily all referring to the same embodiment.

While the exemplary embodiments have been shown and described, it willbe understood that various changes and modifications to the foregoingembodiments may become apparent to those skilled in the art withoutdeparting from the spirit and scope of the present invention.

What is claimed is:
 1. A system to predict service failures in a supplychain using machine learning, comprising: a computer comprising aprocessor and a memory, the computer configured to: receive historicalsupply chain data from an archiving system, the archiving system storinghistorical supply chain data from a supply chain network comprising oneor more supply chain entities; train a prediction model to predict oneor more supply chain events using a sample of the historical supplychain data; predict the one or more supply chain events during aprediction period by applying the trained prediction model, the one ormore supply chain events associated with at least one supply chainentity of the one or more supply chain entities; calculate an occurrencerisk score for at least one of the one or more supply chain events, theoccurrence risk score indicating a possibility that the at least one ofthe one or more supply chain events will occur; calculate precision andrecall scores for the prediction model, wherein the precision scoresindicate a proportion of predicted supply chain events that actuallyoccur, and wherein the recall scores indicate a proportion of supplychain events that occur will be predicted; generate one or more alertsfor the one or more supply chain events, each of the one or more alertsassociated with at least one alert supply chain event of the one or moresupply chain events, the one or more alerts comprising at least onealert item; render, for display on a user interface, a visualizationcomprising one or more selectable user interface elements represented byone or more alert elements, each of the one or more alert elementsassociated with an alert; and provide one or more tools for initiatingone or more corrective actions to be undertaken in order to resolve oneor more underlying causes of the at least one alert supply chain event.2. The system of claim 1, wherein the computer is further configured toprepare the historical supply chain data by: aggregate the historicalsupply chain data at a particular granularity level; normalize thehistorical supply chain data to be quantity independent; and rescale thehistorical supply chain data to compare data of different scopes.
 3. Thesystem of claim 1, wherein the computer is further configured to:display a context of one or more of the alerts and associated factorsidentified by the prediction model contributing to a failure riskcorresponding to the one or more alerts, the associated factorssuggesting one or more corrective actions to prevent the supply chainevent.
 4. The system of claim 1, wherein the computer is furtherconfigured to: in response to the prediction period elapsing, storesupply chain data corresponding to the elapsed prediction period asadditional historical supply chain data; update the prediction modelusing the additional historical supply chain data; and test theperformance of the updated prediction model over a particular timeperiod.
 5. The system of claim 1, wherein the computer is furtherconfigured to: display a performance of the prediction model using aperformance curve corresponding to the calculated precision and recallscores.
 6. The system of claim 1, wherein the computer is furtherconfigured to: in response to a selection of one of the one or moreselectable user interface elements to select an alert, display theprecision and recall scores indicating past performance of theprediction model for the selected alert.
 7. The system of claim 1,wherein the computer is further configured to: deprioritize or notdisplay one or more alerts that are handled by other supply chainplanning systems; or not display one or more alerts by applyingexclusion rules for predicted supply chain events whose resolution isnot actionable within a particular horizon.
 8. A method to predictservice failures in a supply chain using machine learning, comprising:receiving, by a computer comprising a processor and a memory, historicalsupply chain data from a supply chain network comprising one or moresupply chain entities; training, by the computer, a prediction model topredict one or more supply chain events using a sample of the historicalsupply chain data; predicting, by the computer, the one or more supplychain events during a prediction period by applying the trainedprediction model, the one or more supply chain events associated with atleast one supply chain entity of the one or more supply chain entities;calculating, by the computer, an occurrence risk score for at least oneof the one or more supply chain events, the occurrence risk scoreindicating a possibility that the at least one of the one or more supplychain events will occur; calculating, by the computer, precision andrecall scores for the prediction model, wherein the precision scoresindicate a proportion of predicted supply chain events that actuallyoccur, and wherein the recall scores indicate a proportion of supplychain events that occur will be predicted; generating, by the computer,one or more alerts for the one or more supply chain events, each of theone or more alerts associated with at least one alert supply chain eventof the one or more supply chain events, the one or more alertscomprising at least one alert item; rendering, by the computer, fordisplay on a user interface, a visualization comprising one or moreselectable user interface elements represented by one or more alertelements, each of the one or more alert elements associated with analert; and providing, by the computer, one or more tools for initiatingone or more corrective actions to be undertaken in order to resolve oneor more underlying causes of the at least one alert supply chain event.9. The method of claim 8, further comprising preparing the historicalsupply chain data by: aggregating, by the computer, the historicalsupply chain data at a particular granularity level; normalizing, by thecomputer, the historical supply chain data to be quantity independent;and rescaling, by the computer, the historical supply chain data tocompare data of different scopes.
 10. The method of claim 8, furthercomprising: displaying, by the computer, a context of one or more of thealerts and associated factors identified by the prediction modelcontributing to a failure risk corresponding to the one or more alerts,the associated factors suggesting one or more corrective actions toprevent the supply chain event.
 11. The method of claim 8, furthercomprising: in response to the prediction period elapsing, storing, bythe computer, supply chain data corresponding to the elapsed predictionperiod as additional historical supply chain data; updating, by thecomputer, the prediction model using the additional historical supplychain data; and testing, by the computer, the performance of the updatedprediction model over a particular time period.
 12. The method of claim8, further comprising: displaying, by the computer, a performance of theprediction model using a performance curve corresponding to thecalculated precision and recall scores.
 13. The method of claim 8,further comprising: in response to selecting of one of the one or moreselectable user interface elements to select an alert, displaying, bythe computer, the precision and recall scores indicating pastperformance of the prediction model for the selected alert.
 14. Themethod of claim 8, further comprising: deprioritizing or not displaying,by the computer, one or more alerts that are handled by other supplychain planning systems; or not displaying, by the computer, one or morealerts by applying exclusion rules for predicted supply chain eventswhose resolution is not actionable within a particular horizon.
 15. Anon-transitory computer-readable medium embodied with software topredict service failures in a supply chain using machine learning, thesoftware when executed configured to: receive historical supply chaindata from an archiving system, the archiving system storing historicalsupply chain data from a supply chain network comprising one or moresupply chain entities; train a prediction model to predict one or moresupply chain events using a sample of the historical supply chain data;predict the one or more supply chain events during a prediction periodby applying the trained prediction model, the one or more supply chainevents associated with at least one supply chain entity of the one ormore supply chain entities; calculate an occurrence risk score for atleast one of the one or more supply chain events, the occurrence riskscore indicating a possibility that the at least one of the one or moresupply chain events will occur; calculate precision and recall scoresfor the prediction model, wherein the precision scores indicate aproportion of predicted supply chain events that actually occur, andwherein the recall scores indicate a proportion of supply chain eventsthat occur will be predicted; generate one or more alerts for the one ormore supply chain events, each of the one or more alerts associated withat least one alert supply chain event of the one or more supply chainevents, the one or more alerts comprising at least one alert item;render, for display on a user interface, a visualization comprising oneor more selectable user interface elements represented by one or morealert elements, each of the one or more alert elements associated withan alert; and provide one or more tools for initiating one or morecorrective actions to be undertaken in order to resolve one or moreunderlying causes of the at least one alert supply chain event.
 16. Thenon-transitory computer-readable medium of claim 15, wherein thesoftware when executed is further configured to: aggregate thehistorical supply chain data at a particular granularity level;normalize the historical supply chain data to be quantity independent;and rescale the historical supply chain data to compare data ofdifferent scopes.
 17. The non-transitory computer-readable medium ofclaim 15, wherein the software when executed is further configured to:display a context of one or more of the alerts and associated factorsidentified by the prediction model contributing to a failure riskcorresponding to the one or more alerts, the associated factorssuggesting one or more corrective actions to prevent the supply chainevent.
 18. The non-transitory computer-readable medium of claim 15,wherein the software is further configured to: in response to theprediction period elapsing, store supply chain data corresponding to theelapsed prediction period as additional historical supply chain data;update the prediction model using the additional historical supply chaindata; and test the performance of the updated prediction model over aparticular time period.
 19. The non-transitory computer-readable mediumof claim 15, wherein the software is further configured to: display aperformance of the prediction model using a performance curvecorresponding to the calculated precision and recall scores.
 20. Thenon-transitory computer-readable medium of claim 15, wherein thesoftware when executed is further configured to: in response to aselection of one of the one or more selectable user interface elementsto select an alert, display the precision and recall scores indicatingpast performance of the prediction model for the selected alert.